I have trained a model and want to calculate several important metrics such as accuracy, precision, recall, and f1 score.
The process I followed is:
from pyspark.ml.classification import LogisticRegression
lr = LogisticRegression(featuresCol='features',labelCol='label')
lrModel = lr.fit(train)
lrPredictions = lrModel.transform(test)
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.evaluation import BinaryClassificationEvaluator
eval_accuracy = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="accuracy")
eval_precision = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="precision")
eval_recall = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="recall")
eval_f1 = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="f1Measure")
eval_auc = BinaryClassificationEvaluator(labelCol="label", rawPredictionCol="prediction")
accuracy = eval_accuracy.evaluate(lrPredictions)
precision = eval_precision.evaluate(lrPredictions)
recall = eval_recall.evaluate(lrPredictions)
f1score = eval_f1.evaluate(lrPredictions)
auc = eval_accuracy.evaluate(lrPredictions)
However, it can only calculate accuracy and auc, but not the three others. What should I modify here?
According to the docs, for the F1 measure, precision, and recall, the relevant arguments of MulticlassClassificationEvaluator should be respectively
metricName="f1"
metricName="precisionByLabel"
metricName="recallByLabel"